Improving Service Recommendation by Alleviating the Sparsity with a Novel Ontology-Based Clustering

Abstract

Web service recommendation in an efficient and accurate manner has become a significant tool with information overload and an increasingly urgent demand to provide appropriate recommendations to users. Among the service recommendation algorithms, Collaborative Filtering (CF) gives credence to user inputs by comparing user's correlations. Performance of the service recommendation approaches becomes deficient due to the data sparsity and cold-start issues, which make the incomplete and inadequate information to analyze a user predicament on Web services. This paper proposes a CF-based recommendation approach that first alleviates the sparsity problem using a novel ontology-based clustering approach that used domain specificity and service similarity for the ontology generation. Then, we propose a trustbased user rating prediction by determining the trust value between users by calculating the correlation of users. The experimental results indicate that the proposed approach can effectively alleviate the sparsity and cold-start problems by lower prediction error compared with existing sparsity managing mechanisms in service recommendations.